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cellranger human reference sequence v2020 a  (10X Genomics)

 
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    10X Genomics cellranger human reference sequence v2020 a
    Cellranger Human Reference Sequence V2020 A, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/human+reference+sequences/cellranger/pm40562033-351-22-27
    Average 86 stars, based on 1 article reviews
    cellranger human reference sequence v2020 a - by Bioz Stars, 2026-10
    86/100 stars

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    Related Articles

    Sequencing:

    Article Title: High-throughput identification of Toxoplasma gondii effector proteins that target host cell transcription.
    Article Snippet: Pre-processing, filtering, and normalisation A multiple species reference genome comprising Homo sapiens GRCh38 release 95 and Toxoplasma gondiiME49 TGA4 release 42 was generated using Cell Ranger v3.0.2 (10x Genomics). .. Using Cell Ranger v6.1.2 (10x Genomics), sequencing reads from the 3’ mRNA and CRISPR sgRNA libraries were aligned to this reference and a custom reference of protospacer sequences, including intronic reads but leaving all other settings as the default. .. Cell Ranger aligns the reads to the transcriptome/protospacer sequence references, assigns reads to a cell barcode, and counts the number of UMIs for each gene/protospacer assigned to each cell barcode to generate a UMI x cell barcode count matrices (for further details see support.10xgenomics.com).

    CRISPR:

    Article Title: High-throughput identification of Toxoplasma gondii effector proteins that target host cell transcription.
    Article Snippet: Pre-processing, filtering, and normalisation A multiple species reference genome comprising Homo sapiens GRCh38 release 95 and Toxoplasma gondiiME49 TGA4 release 42 was generated using Cell Ranger v3.0.2 (10x Genomics). .. Using Cell Ranger v6.1.2 (10x Genomics), sequencing reads from the 3’ mRNA and CRISPR sgRNA libraries were aligned to this reference and a custom reference of protospacer sequences, including intronic reads but leaving all other settings as the default. .. Cell Ranger aligns the reads to the transcriptome/protospacer sequence references, assigns reads to a cell barcode, and counts the number of UMIs for each gene/protospacer assigned to each cell barcode to generate a UMI x cell barcode count matrices (for further details see support.10xgenomics.com).



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    Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, human/virus sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent protein biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein sequences, with outputs ranked to return the top five highest-confidence matches.

    Journal: Biosafety and Health

    Article Title: DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models

    doi: 10.1016/j.bsheal.2025.07.005

    Figure Lengend Snippet: Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, human/virus sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent protein biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein sequences, with outputs ranked to return the top five highest-confidence matches.

    Article Snippet: Since model-generated sequences may not correspond to natural proteins, we retrieved 20,078 human reference protein sequences from National Center of Biotechnology Information (NCBI) (accession: GCF_000001405.40) and 17,451 reviewed viral protein sequences from UniProt Swiss-Pro (taxon ID 10239) to ensure biological relevance.

    Techniques: Virus, Sequencing

    Cosine similarity analysis of generated human protein sequences and viral protein sequences. A) Cosine similarity between generated and ground-truth human proteins, illustrating the distribution of cosine similarity values between human protein sequences. B) Cosine similarity between generated and ground-truth viral proteins, depicting the distribution for viral protein sequences, which exhibits slightly lower and more variable similarity scores. In both cases, the distributions are sharply peaked around 0.8, indicating a generally strong semantic alignment across samples. Human and viral protein sequences from the test set were analyzed using DeepHVI to generate a density distribution of similarity scores between reconstructed sequences and human interactors. Abbreviations: Std, standard deviation; Min, minimum; Max, maximum.

    Journal: Biosafety and Health

    Article Title: DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models

    doi: 10.1016/j.bsheal.2025.07.005

    Figure Lengend Snippet: Cosine similarity analysis of generated human protein sequences and viral protein sequences. A) Cosine similarity between generated and ground-truth human proteins, illustrating the distribution of cosine similarity values between human protein sequences. B) Cosine similarity between generated and ground-truth viral proteins, depicting the distribution for viral protein sequences, which exhibits slightly lower and more variable similarity scores. In both cases, the distributions are sharply peaked around 0.8, indicating a generally strong semantic alignment across samples. Human and viral protein sequences from the test set were analyzed using DeepHVI to generate a density distribution of similarity scores between reconstructed sequences and human interactors. Abbreviations: Std, standard deviation; Min, minimum; Max, maximum.

    Article Snippet: Since model-generated sequences may not correspond to natural proteins, we retrieved 20,078 human reference protein sequences from National Center of Biotechnology Information (NCBI) (accession: GCF_000001405.40) and 17,451 reviewed viral protein sequences from UniProt Swiss-Pro (taxon ID 10239) to ensure biological relevance.

    Techniques: Generated, Standard Deviation